On the anaconda prompt, do a
- `conda -V` or `conda --version`  to get the conda version.
- `python -V` or `python --version` to get the python version.
- `conda list anaconda$` to get the Anaconda version.
- `conda list` to get the Name, Version, Build &amp; Channel details of all the packages installed (in the current environment).
- `conda info` to get all the current environment details.
- `conda info --envs` To see a list of all your environments  


Detailed description [here][1], download cheat sheet from [here][2]


  [1]: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#
  [2]: https://conda.io/projects/conda/en/latest/user-guide/cheatsheet.html

The folder containing your Anaconda installation contains a subfolder called `conda-meta` with json files for all installed packages, including one for Anaconda itself. Look for `anaconda-&lt;version&gt;-&lt;build&gt;.json`.

My file is called `anaconda-5.0.1-py27hdb50712_1.json`, and at the bottom is more info about the version:

    &quot;installed_by&quot;: &quot;Anaconda2-5.0.1-Windows-x86_64.exe&quot;, 
    &quot;link&quot;: { &quot;source&quot;: &quot;C:\\ProgramData\\Anaconda2\\pkgs\\anaconda-5.0.1-py27hdb50712_1&quot; }, 
    &quot;name&quot;: &quot;anaconda&quot;, 
    &quot;platform&quot;: &quot;win&quot;, 
    &quot;subdir&quot;: &quot;win-64&quot;, 
    &quot;url&quot;: &quot;https://repo.continuum.io/pkgs/main/win-64/anaconda-5.0.1-py27hdb50712_1.tar.bz2&quot;, 
    &quot;version&quot;: &quot;5.0.1&quot;

(Slightly edited for brevity.)

The output from `conda -V` is the conda version.

If you want to check the python version in a particular conda environment you can also use ```conda list python```

I am creating a simple app using `React Native` for both `iOS` and `Android`. I&#39;ve tested both platforms for a while and all works very well. But recently, I was focusing on `iOS` and all my functionalities are working perfectly so far. But when I tried it on `Android`; specifically when I tried to add an item to my existing local data in `AsyncStorage` it gives me the following error:

    illegal number of arguments for updateHotspot command

My app has also other functions that ads item using `AsyncStorage` with no problem. But again everything works fine in `iOS`.

I&#39;m using `Genymotion` (trial version) to test it in an `Android` platform.

I tried to search on the Internet but found nothing. It seems to me it&#39;s a `Java` thing.

**Update**:

I am no longer using Genymotion (trial version expired) but just the emulator from Android Studio this time.

This is the error that I received:

[![enter image description here][1]][1]

  [1]: https://i.stack.imgur.com/6xn97.png

React Native Android run causes &quot;Illegal number of arguments for updateHotspot command&quot;

I use material ui v0.20.0 and I have to prohibit saving the password for a user with TextField. I added props to TextField autocomplete=&#39;nope&#39; cause not all the browsers understand autocomplete=&#39;off&#39;. It seems that the last version of Chrome 63 does not accept it. Sometimes it does not work and sometimes it does. I can not get why it works so hectic. When chrome asks to save password and I save it, and after that I want to edit input I still have this : [![enter image description here][1]][1] 

      &lt;TextField
             name=&#39;userName&#39;
             floatingLabelText={&lt;FormattedMessage id=&#39;users.username&#39; /&gt;}
             value={name || &#39;&#39;}
             onChange={(e, name) =&gt; this.changeUser({name})}
             // autoComplete=&#39;new-password&#39;
    
        /&gt; 

        &lt;TextField
            name=&#39;password&#39;
            floatingLabelText={&lt;FormattedMessage id=&#39;users.passwords.new&#39; /&gt;}
            type=&#39;password&#39;
            value={password || &#39;&#39;}
            onChange={(e, password) =&gt; this.changeUser({password})}
            autoComplete=&#39;new-password&#39;
       /&gt;


Looks like it works in Firefox(v57.0.4)

By default TextField does not have autoComplete=&#39;off&#39;
[![enter image description here][2]][2]


  [1]: https://i.stack.imgur.com/9NZgs.png
  [2]: https://i.stack.imgur.com/5edIQ.png

material-ui TextField disable Browser autoComplete

I have a Windows 10 PC with python anaconda installed. The latest anaconda version is v5.0.1

I would like to find out whether the PC has the latest version v5.0.1 installed and whether it is 32-bit/64bit or python 2.7/3.6. How do I do that?

https://www.anaconda.com/download/

How to check python anaconda version installed on Windows 10 PC?

I have a Windows 10 PC with python anaconda installed. The latest anaconda version is v5.0.1
I would like to find out whether the PC has the latest version v5.0.1 installed and whether it is 32-bit/64bit or python 2.7/3.6. How do I do that?
<a href="https://www.anaconda.com/download/" target="_blank" rel="noopener noreferrer">https://www.anaconda.com/download/</a>

The problem is simple: I have some data on gDrive, for example at 
`/projects/my_project/my_data*`.

Also I have a simple notebook in gColab.

So, I would like to do something like:

    for file in glob.glob(&quot;/projects/my_project/my_data*&quot;):
        do_something(file)

Unfortunately, all examples (like this - https://colab.research.google.com/notebook#fileId=/v2/external/notebooks/io.ipynb, for example) suggests to only mainly load all necessary data to notebook.

But, if I have a lot of pieces of data, it can be quite complicated. 
Is there any opportunities to solve this issue?

Thanks for help!


Google Colab: how to read data from my google drive?

I&#39;ve noticed three methods of selecting a column in a Pandas DataFrame:

**First method of selecting a column using loc:**

    df_new = df.loc[:, &#39;col1&#39;]

**Second method - seems simpler and faster:**

    df_new = df[&#39;col1&#39;]

**Third method - most convenient:**

    df_new = df.col1

Is there a difference between these three methods? I don&#39;t think so, in which case I&#39;d rather use the third method.

I&#39;m mostly curious as to why there appear to be three methods for doing the same thing.

What is the difference between using loc and using just square brackets to filter for columns in Pandas/Python?

I can&#39;t see the tqdm progress bar when I use this code to iterate my opened file:

            with open(file_path, &#39;r&#39;) as f:
            for i, line in enumerate(tqdm(f)):
                if i &gt;= start and i &lt;= end:
                    print(&quot;line #: %s&quot; % i)
                    for i in tqdm(range(0, line_size, batch_size)):
                        # pause if find a file naed pause at the currend dir
                        re_batch = {}
                        for j in range(batch_size):
                            re_batch[j] = re.search(line, last_span)
what&#39;s the right way to use tqdm here?

Python enumerate() tqdm progress-bar when reading a file?

In JS I can do this

    const a = [1,2,3,4]
    const b = [10, ...a]
    console.log(b) // [10,1,2,3,4]

Is there a similar way in python?


How to spread a python array

Let&#39;s assume we have a bunch of links to download and each of the link may take a different amount of time to download. And I&#39;m allowed to download using utmost 3 connections only. Now, I want to ensure that I do this efficiently using asyncio.

Here&#39;s what I&#39;m trying to achieve: At any point in time, try to ensure that I have atleast 3 downloads running.

    Connection 1: 1---------7---9---
    Connection 2: 2---4----6-----
    Connection 3: 3-----5---8-----

The numbers represent the download links, while hyphens represent Waiting for download.

Here is the code that I&#39;m using right now

    from random import randint
    import asyncio
    
    count = 0
    
    
    async def download(code, permit_download, no_concurrent, downloading_event):
        global count
        downloading_event.set()
        wait_time = randint(1, 3)
        print(&#39;downloading {} will take {} second(s)&#39;.format(code, wait_time))
        await asyncio.sleep(wait_time)  # I/O, context will switch to main function
        print(&#39;downloaded {}&#39;.format(code))
        count -= 1
        if count &lt; no_concurrent and not permit_download.is_set():
            permit_download.set()
    
    
    async def main(loop):
        global count
        permit_download = asyncio.Event()
        permit_download.set()
        downloading_event = asyncio.Event()
        no_concurrent = 3
        i = 0
        while i &lt; 9:
            if permit_download.is_set():
                count += 1
                if count &gt;= no_concurrent:
                    permit_download.clear()
                loop.create_task(download(i, permit_download, no_concurrent, downloading_event))
                await downloading_event.wait()  # To force context to switch to download function
                downloading_event.clear()
                i += 1
            else:
                await permit_download.wait()
        await asyncio.sleep(9)
    
    if __name__ == &#39;__main__&#39;:
        loop = asyncio.get_event_loop()
        try:
            loop.run_until_complete(main(loop))
        finally:
            loop.close()

And the output is as expected:

    downloading 0 will take 2 second(s)
    downloading 1 will take 3 second(s)
    downloading 2 will take 1 second(s)
    downloaded 2
    downloading 3 will take 2 second(s)
    downloaded 0
    downloading 4 will take 3 second(s)
    downloaded 1
    downloaded 3
    downloading 5 will take 2 second(s)
    downloading 6 will take 2 second(s)
    downloaded 5
    downloaded 6
    downloaded 4
    downloading 7 will take 1 second(s)
    downloading 8 will take 1 second(s)
    downloaded 7
    downloaded 8

But here are my questions:

1. At the moment, I&#39;m simply waiting for 9 seconds to keep the main function running till the downloads are complete. Is there an efficient way of waiting for the last download to complete before exiting the `main` function? (I know there&#39;s `asyncio.wait`, but I&#39;ll need to store all the task references for it to work)

2. What&#39;s a good library that does this kind of task? I know javascript has a lot of async libraries, but what about Python?

Edit:
2. What&#39;s a good library that takes care of common async patterns? (Something like [async][1])


  [1]: https://www.npmjs.com/package/async

How to limit concurrency with Python asyncio?

I am using the conda package manager - a lot. By now I have quite a few environments and a lot of downloaded packages taking a lot of space on my SSD. An obvious path to free some of that space is to use the command

    conda env export &gt; environment.yml

from https://conda.io/docs/user-guide/tasks/manage-environments.html#exporting-the-environment-file to export which packages my old, inactive projects use(d) and then delete these environments. As far as I understand, this should free some of the space in `anaconda2/envs/`, but not in `anaconda2/pkgs/`. How do I get rid of these packages? Also, I suspect that there might be quite a few packages still sitting around, to which no environment is linking to - could that happen?

Questions:

1. In general: What is the best way to reduce the space taken up by conda?
2. How do I get rid of packages that no environment is using anymore? How do I prune my packages? I am searching for something like `sudo apt-get autoremove` from Ubuntu/Debian.



How to free disk space taken up by (ana)conda?

I have a file ```mysql.py```, which I use in almost all of my projects. Since I do not want to copy and paste the same file into each of these projects I wrote a module - possibly a package in the future. 

# Question
How do I add a local module to my conda environment, and automatically update or watch it when I change something in ```mysql.py```? How to do the same for a package?

I would like to avoid to set up a local channel/repository and just reference to the folder with ```mysql.py```.

How to install my own python module (package) via conda and watch its changes

So I am used to typing `source activate &lt;environment&gt;` when starting a python Anaconda environment. That works just fine. But when I create new conda environments I am seeing the message on Ubuntu 16.04 to start the environments with `conda activate` instead. Besides the errors about how to set up my shell to use `conda activate` instead, I am still not clear on what is the difference between `source activate ...` and `conda activate ...` Is there a reason to change? Does anyone know the difference between these two commands? Thanks.


Python Anaconda: should I use `conda activate` or `source activate` in linux

I&#39;d like to install the pymongo library but I&#39;m getting the following error:

        (C:\Users\xxxxxxx\AppData\Local\Continuum\anaconda3) C:\Users\xxxxxxx&gt;
        conda install -c anaconda pymongo

        Fetching package metadata ...
        CondaHTTPError: HTTP 000 CONNECTION FAILED for url &lt;https://conda.anaconda.org/a
        naconda/win-64/repodata.json&gt;
        Elapsed: -
        
        An HTTP error occurred when trying to retrieve this URL.
        HTTP errors are often intermittent, and a simple retry will get you on your way.
        
        ConnectTimeout(MaxRetryError(&quot;HTTPSConnectionPool(host=&#39;conda.anaconda.org&#39;, por
        t=443): Max retries exceeded with url: /anaconda/win-64/repodata.json (Caused by
         ConnectTimeoutError(&lt;urllib3.connection.VerifiedHTTPSConnection object at 0x000
        00000054D6128&gt;, &#39;Connection to conda.anaconda.org timed out. (connect timeout=9.
        15)&#39;))&quot;,),)

Steps taken to resolve:

    1. Update C:\Users\\xxxxxxx\.condarc file with the following:
    
    channels:
      - defaults
    ssl_verify: false
    proxy_servers:
    http: http://sproxy.fg.xxx.com:1000
    https: https://sproxy.fg.xxx.com:1000

    2. (C:\Users\xxxxxxx\AppData\Local\Continuum\anaconda3) C:\Users\xxxxxxx&gt;
        conda config --set ssl_verify False

Additional Info:

    (C:\Users\xxxxxxx\AppData\Local\Continuum\anaconda3) C:\Users\xxxxxxx&gt;
    conda info
    
    Current conda install:
    
                   platform : win-64
              conda version : 4.3.27
           conda is private : False
          conda-env version : 4.3.27
        conda-build version : 3.0.22
             python version : 3.6.2.final.0
           requests version : 2.18.4
                config file : C:\Users\xxxxxxx\.condarc
                 netrc file : None
               offline mode : False
                 user-agent : conda/4.3.27 requests/2.18.4 CPython/3.6.2 Windows/7 W
    indows/6.1.7601
              administrator : False

A number of posts online simply reinstalled Anaconda, any other options apart from a fresh install?


Issues with installing python libraries on Windows : CondaHTTPError: HTTP 000 CONNECTION FAILED for url &lt;https://conda.anaconda.org/anaconda/win-64

What is the conda version of this?

    pip install -r requirements.txt --target ./lib

I&#39;ve found these commands:

    while read requirement; do conda install --yes $requirement; done &lt; requirements.txt


But it doesn&#39;t tell how to specify `--target ./lib`



Content Type	Original Author	Original Content on Stackoverflow
Question	user1315789	View Question on Stackoverflow
Solution 1 - Python	Shijith	View Answer on Stackoverflow
Solution 2 - Python	kthy	View Answer on Stackoverflow
Solution 3 - Python	Mwibutsa Floribert	View Answer on Stackoverflow

How to check python anaconda version installed on Windows 10 PC?

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material-ui TextField disable Browser autoComplete

React Native Android run causes "Illegal number of arguments for updateHotspot command"

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